What am I doing right now? Internship.. Studies.. or Both?!

My experience of hunting for and landing an internship in Singapore.

Bhagavad Gita

सुखदु:खे समे कृत्वा लाभालाभौ जयाजयौ |

ततो युद्धाय युज्यस्व नैवं पापमवाप्स्यसि ||

Chapter 2 Verse 38, Bhagavad Gita

Shree Krishna says Fight for the sake of duty, treating alike happiness and distress, loss and gain, victory and defeat. Fulfilling your duty and responsibility in this way, you will never incur sin.

Arjuna’s was apprehensive that by killing his enemies, he would incur sin. Shree Krishna addresses his apprehension and he advises him to do his duty (dharma), without attachment to the fruits of his action. Such an attitude will release him from any sinful reactions.

To be honest, I was/am not planning to kill anybody as in Mahabharat. However, as soon as my semester at NUS started in August, I knew that getting into a comfort zone is going to be my biggest enemy and if I don’t kill it, I will become complacent. Therefore, I began my hunt for a good internship opportunity early in the semester. Over a period of time, when you just apply and apply, and don’t hear from anyone, it’s a little disheartening. Some wanted immediate joining, some were looking for summer interns, etc. However, luck turned its tide and soon, I started getting online test invites. I knew that good performance in online coding tests usually translates to face-to-face (f2f) interviews and I was fortunate enough to be able to crack almost all the coding tests.

Coding Test Format

Most of the firms had two coding questions: a general algorithmic question (I used to code in Python) and a SQL coding question. Some of the firms also gave MCQs related to product analysis, data analysis, probability, etc along with the two coding questions.

Interview Experience and Format

The f2f interviews, majorly test whether the candidate is genuine in portraying his/her persona and knowledge by walking through the resume and the coding test. Below, I will give an account of 2 of my 3 interesting interview experiences of which I accepted one.

All of my f2f interviews were heavily concentrated on my past experience. All except Grab, wherein I had two f2f interviews. The interviewer in my first f2f interview dug into my past Software Development experience and very subtly steered the conversation to a made up scenario based problem solving session. I was asked to do a storyboarding concept of how can I visualize customers pain points on the app, given I have unlimited access to all kind of data. This was an open ended question which had numerous possibilities and not one correct approach. I was literally drained of energy in my second f2f experience, which I took remotely from my home (in India). The interviewer was a entrepreneur himself, and he was pretty interested in my past entrepreneurial journey and learnings.

I also appeared for a coding test (on codility) and an f2f interview for Global Data Innovation Center (GDIC) at Dentsu Aegis Network, Singapore. My f2f interview was taken by two senior members of the GDIC and they grilled me quite well on my past experience. I guess, with good scores in coding tests, and a good past experience of doing substantial work back in India helped me crack almost all the f2f interviews. I subsequently received offer from 2 firms, and finalized my internship offer at GDIC, Denstu Aegis Network.

Internship Experience Preview

With a long commitment (full time during December and part-time from January to April) in sight, I began my internship in December 2018. I initially worked on scouting and building a POC for a cloud agnostic, open source API management tool/platform which could help in setting up API design, gateway, store, and analytics. Since it’s all Open Source, I will probably compile all my findings in a separate blog. This project helped me in getting acquainted with the code base and I could leverage domain knowledge of APIs and API management from my previous Software Development experience.

Sharing the knowledge that I gained through my first project at GDIC

After completing the first assignment and getting acquainted to the projects in GDIC, I was asked to contribute in one of the GDIC’s ambitious, research oriented project which was quite unprecedented as per the competitive landscape. Therefore, I began my journey in Project Pearl. What’s Project Pearl? Well..I promise that it’s going to be an interesting read as well and I will continue that in my next blog. So stay tuned!

Thanks for reading and I have written other posts related to software engineering and data science as well. You might want to check them out here. You can also subscribe to my blog to receive updates straight in your inbox.

Multi Class Classification in Text using R: Predicting Ted Talk Ratings

Multi Class Classification in Text

This blog is in continuation to my NLP blog series. In the previous blogs, I discussed data pre-processing steps in R and recognizing emotions present in ted talks. In this blog, I am going to predict the ratings of the ted talks given by viewers. This would require Multi Class Classification and quite a bit of data cleaning and preprocessing. We will discuss each step in detail below. So, let’s dive in.

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Emotions in Ted Talks: Text Analytics in R

Image result for emotions nlp

This post is in continuation with my NLP blog series. You might want to checkout my previous blog in which I discussed data pre-processing in R. In this blog, I will determine the emotions in the Ted Talks. At the end, I will compute a HeatMap of emotions and talks to aid in our visualization.

So, without further ado, let’s dive in!

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Data Preprocessing in R

Image result for nlp

I have recently got my hands dirty with Natural Language Processing (NLP). I know, it’s a little late to the party but I am at least in the party!

To start with a general overview, I implemented quite a few tasks related to NLP including Text Classification, Document Similarity, Part-of-Speech (POS) Tagging, Emotion Recognition, etc. These tasks were made possible by implementing text pre-processing (noise removal, stemming) and text to features (TF-IDF, N-Grams, Topic Modeling, etc). I implemented these in both R and Python. So, I will try to jot down my experiences in both of these environments. Therefore, I will write this as a blog series, wherein each blog will discuss only one particular thing implemented in one particular environment.

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DataKind Singapore

I have recently shifted gears in my life. A shift to academics after spending quite sometime in the industry has been equally exciting and challenging at the same time. Even with all the diversity in the cohort of my own program (in terms of work experience and country of origin) along with a campus hustling with activities all day, I wanted to explore the communities ingrained deep within the culture of Singapore. A natural choice for me, was to look for meetups.

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My learning experience with Google’s Machine Learning Crash Course

Google’s Machine Learning Crash Course (MLCC)

I came to know about Google’s Machine Learning Crash Course (MLCC) from Sundar Pichai’s tweet. I then enquired about it with some close acquaintances working in Google. I was soon pretty convinced of pursuing this course, after their good words about it and my own research on the course content. This post is going to be an account of my learnings from MLCC. I will structure the learnings in such a way that it will look more like a review. I will also include what I really liked about the course and things which I think they can possibly improve, if the creators are planning to update the course content. So, let’s get started!

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Engineering challenges of streaming a million concurrent JSON data streams from product to CRM

At Truebil, I was fortunate enough to be given an opportunity to solve a unique engineering problem. We had already outsourced CRM development to a third party but I had to integrate the data flow to CRM from our product and back. I mentioned earlier that I was given a unique engineering problem because of the challenges it posed. The challenge didn’t lie in the CRM integration alone, but the fact that Truebil has umpteen number of in-house products which spawn data close to about 1 Million data streams per hour. Besides building a bastion of such magnitude, I knew that it would be equally challenging to work with three different verticals and stakeholders. This post is going to be an account of my experiences dealing with two things. First, how Truebil catered to the transfer of a million data stream to and from CRM without hampering its operations and customer support. The second experience deals in the art of working with different verticals. Not that I have mastered the art or something, but I will share my own experiences and learnings in this post.



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